init

model_executor/models/aya_vision.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project Adapted from
+# https://github.com/huggingface/transformers/tree/main/src/transformers/models/aya_vision
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Literal, Optional, TypedDict, Union, cast
+
+import torch
+from torch import nn
+from transformers import BatchFeature, GotOcr2ImageProcessor
+from transformers.activations import ACT2FN
+from transformers.image_processing_utils import get_size_dict
+from transformers.models.aya_vision import AyaVisionConfig
+from transformers.models.aya_vision.processing_aya_vision import (
+    AyaVisionProcessor)
+from transformers.models.got_ocr2.image_processing_got_ocr2 import (
+    get_optimal_tiled_canvas)
+
+from vllm.config import VllmConfig
+from vllm.jsontree import json_map_leaves
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import MultiModalDataDict, MultiModalKwargs
+from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
+                                   MultiModalDataItems)
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo,
+                                        MultiModalFieldConfig,
+                                        PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .siglip import SiglipVisionModel
+from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
+
+
+class AyaVisionImagePixelInputs(TypedDict):
+    type: Literal["pixel_values"]
+    pixel_values: torch.Tensor
+    """
+    Shape: `(num_patches_total, num_channels, height, width)`
+
+    `num_patches_total` is the total number of patches over each image over each
+    prompt in the batch.
+    """
+
+    num_patches: torch.Tensor
+    """Shape: `(batch_size * num_images)`"""
+
+
+class AyaVisionMultiModalProjector(nn.Module):
+
+    def __init__(self, config: AyaVisionConfig):
+        super().__init__()
+        self.config = config
+        self.downsample_factor = config.downsample_factor
+        self.alignment_intermediate_size = getattr(
+            config, "alignment_intermediate_size",
+            config.text_config.hidden_size)
+        self.layernorm = nn.LayerNorm(config.vision_config.hidden_size *
+                                      (config.downsample_factor**2),
+                                      eps=config.adapter_layer_norm_eps)
+
+        self.linear_1 = nn.Linear(
+            config.vision_config.hidden_size * (config.downsample_factor**2),
+            self.alignment_intermediate_size,
+            bias=True,
+        )
+
+        self.act = ACT2FN["silu"]  # SwiGLU uses SiLU activation
+        # For SwiGLU, project down to half size since we split intermediate dim
+        self.linear_2 = nn.Linear(self.alignment_intermediate_size // 2,
+                                  config.text_config.hidden_size,
+                                  bias=True)
+
+    def forward(self, image_features: torch.Tensor) -> torch.Tensor:
+        image_features = self.pixel_shuffle(image_features)
+        image_features = self.layernorm(image_features)
+        hidden_states = self.linear_1(image_features)
+
+        # Split along last dimension and apply SwiGLU
+        x, gate = hidden_states.chunk(2, dim=-1)
+        hidden_states = self.act(gate) * x
+
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+    def pixel_shuffle(self,
+                      image_features: torch.Tensor) -> torch.Tensor:  # B, S, D
+        batch_size, seq_length, _ = image_features.shape
+        height = width = int(seq_length**0.5)
+        image_features = image_features.reshape(image_features.shape[0], width,
+                                                height, -1)
+        channels = image_features.shape[-1]
+        image_features = image_features.reshape(
+            batch_size, width, int(height / self.downsample_factor),
+            int(channels * self.downsample_factor))
+        image_features = image_features.permute(0, 2, 1, 3)
+        image_features = image_features.reshape(
+            batch_size, int(height / self.downsample_factor),
+            int(width / self.downsample_factor), -1)
+        image_features = image_features.permute(0, 2, 1, 3)
+        return image_features
+
+
+class AyaVisionProcessingInfo(BaseProcessingInfo):
+
+    def get_hf_config(self) -> AyaVisionConfig:
+        return self.ctx.get_hf_config(AyaVisionConfig)
+
+    def get_hf_processor(self, **kwargs: object) -> AyaVisionProcessor:
+        processor = self.ctx.get_hf_processor(AyaVisionProcessor, **kwargs)
+
+        # Temporary workaround since this processor has multiple image tokens
+        # See https://github.com/huggingface/transformers/issues/38350
+        processor._check_special_mm_tokens = lambda *args, **kwargs: None
+
+        return processor
+
+    def get_image_processor(self) -> GotOcr2ImageProcessor:
+        return self.get_hf_processor().image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        image_processor = self.get_image_processor()
+        height = image_processor.size['height']
+        width = image_processor.size['width']
+        max_patches = image_processor.max_patches
+        return ImageSize(height=height * max_patches,
+                         width=width * max_patches)
+
+    def get_num_patches(self, *, image_width: int, image_height: int,
+                        size: dict, min_patches: int, max_patches: int) -> int:
+        """
+        Calculate the number of patches needed for a given image based on size
+        constraints.  This method replicates and adjusts the logic from:
+        transformers/models/got_ocr2/image_processing_got_ocr2
+        """
+        size = get_size_dict(size, default_to_square=False)
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (image_height, image_width), (size["height"], size["width"]),
+            min_patches, max_patches)
+        num_blocks = num_columns * num_rows
+        return num_blocks if num_blocks == 1 else num_blocks + 1
+
+
+class AyaVisionDummyInputsBuilder(
+        BaseDummyInputsBuilder[AyaVisionProcessingInfo]):
+
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        processor = self.info.get_hf_processor()
+        image_token = processor.image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> MultiModalDataDict:
+        num_images = mm_counts.get("image", 0)
+        image_size = \
+            self.info.get_image_size_with_most_features()
+
+        return {
+            "image":
+            self._get_dummy_images(width=image_size.width,
+                                   height=image_size.height,
+                                   num_images=num_images)
+        }
+
+
+class AyaVisionMultiModalProcessor(
+        BaseMultiModalProcessor[AyaVisionProcessingInfo]):
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        processed_outputs = super()._call_hf_processor(
+            prompt,
+            mm_data,
+            mm_kwargs,
+        )
+        hf_processor = self.info.get_hf_processor(**mm_kwargs)
+        image_processor = hf_processor.image_processor
+
+        # HF processor pops the `num_patches` kwarg, which is needed by vLLM
+        if (images := mm_data.get("images")) is not None:
+            parsed_images = (self._get_data_parser().parse_mm_data({
+                "image":
+                images
+            }).get_items("image", ImageProcessorItems))
+            image_sizes = [
+                parsed_images.get_image_size(i)
+                for i in range(len(parsed_images))
+            ]
+
+            num_patches = [
+                self.info.get_num_patches(
+                    image_width=image_size.width,
+                    image_height=image_size.height,
+                    size=image_processor.size,
+                    min_patches=image_processor.min_patches,
+                    max_patches=image_processor.max_patches)
+                for image_size in image_sizes
+            ]
+            processed_outputs["num_patches"] = torch.tensor(num_patches)
+
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        num_patches = hf_inputs.get("num_patches", torch.empty(0))
+        return dict(
+            pixel_values=MultiModalFieldConfig.flat_from_sizes(
+                "image", num_patches),
+            num_patches=MultiModalFieldConfig.batched("image"),
+            image_embeds=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_token = hf_processor.image_token
+        img_patch_token = hf_processor.img_patch_token
+        image_processor = hf_processor.image_processor
+
+        def get_replacement(item_idx: int):
+            images: ImageProcessorItems = mm_items.get("image",
+                                                       ImageProcessorItems)
+            image_size: ImageSize = images.get_image_size(item_idx)
+            num_patches = self.info.get_num_patches(
+                image_width=image_size.width,
+                image_height=image_size.height,
+                size=image_processor.size,
+                min_patches=image_processor.min_patches,
+                max_patches=image_processor.max_patches,
+            )
+            repl = hf_processor._prompt_split_image(num_patches=num_patches)
+
+            return PromptUpdateDetails.select_text(repl, img_patch_token)
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=image_token,
+                replacement=get_replacement,
+            )
+        ]
+
+
+def _get_num_hidden_layers(hf_config: AyaVisionConfig) -> int:
+    feature_layers = hf_config.vision_feature_layer
+    num_hidden_layers = hf_config.vision_config.num_hidden_layers
+    # If we have one feature layer, initialize up to that layer
+    if isinstance(feature_layers, int):
+        return _get_layer_index(feature_layers, num_hidden_layers)
+    # If we have multiple feature layers, initialize up to the deepest m
+    elif isinstance(feature_layers, (list, tuple)):
+        return max(
+            _get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
+    raise TypeError(f"vision_layer_feature type: {type(feature_layers)}"
+                    " is not supported")
+
+
+def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
+    if feature_layer_index < 0:
+        return num_hidden_layers + feature_layer_index + 1
+    return feature_layer_index
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    AyaVisionMultiModalProcessor,
+    info=AyaVisionProcessingInfo,
+    dummy_inputs=AyaVisionDummyInputsBuilder)
+class AyaVisionForConditionalGeneration(nn.Module, SupportsMultiModal,
+                                        SupportsPP):
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config: AyaVisionConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        multimodal_config = vllm_config.model_config.multimodal_config
+        num_hidden_layers = _get_num_hidden_layers(config)
+        self.config = config
+        self.quant_config = quant_config
+        self.multimodal_config = multimodal_config
+
+        self.vision_tower = SiglipVisionModel(
+            config.vision_config,
+            quant_config,
+            num_hidden_layers_override=num_hidden_layers,
+            prefix=maybe_prefix(prefix, "vision_model"))
+        self.vocab_size = config.text_config.vocab_size
+        self.multi_modal_projector = AyaVisionMultiModalProjector(config)
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            hf_config=config.text_config,
+            prefix=maybe_prefix(prefix, "model"),
+            # Cohere2ForCausalLM and CohereForCausalLM are the same on vllm
+            architectures=["Cohere2ForCausalLM"])
+
+    @property
+    def dtype(self):
+        return next(self.parameters()).dtype
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights)
+
+    def _image_pixels_to_features(self, vision_tower: SiglipVisionModel,
+                                  pixel_values: torch.Tensor,
+                                  **kwargs) -> torch.Tensor:
+        target_dtype = vision_tower.get_input_embeddings().weight.dtype
+        image_features = vision_tower(pixel_values.to(dtype=target_dtype),
+                                      **kwargs)
+
+        def select_features(leaf: torch.Tensor):
+            return self._select_image_features(
+                leaf,
+                strategy=self.config.vision_feature_select_strategy,
+            )
+
+        return cast(
+            Union[torch.Tensor, tuple[torch.Tensor, ...]],
+            json_map_leaves(select_features, image_features),
+        )
+
+    def _select_image_features(self, image_features: torch.Tensor, *,
+                               strategy: str) -> torch.Tensor:
+        if strategy == "default":
+            return image_features[:, 1:]
+        elif strategy == "full":
+            return image_features
+
+        raise ValueError(f"Unexpected select feature strategy: {strategy}")
+
+    def _process_image_input(self, image_input: AyaVisionImagePixelInputs,
+                             **kwargs) -> list[torch.Tensor]:
+        assert self.vision_tower is not None
+        pixel_values = image_input["pixel_values"]
+        num_patches = image_input["num_patches"]
+        image_features = self._image_pixels_to_features(
+            self.vision_tower, pixel_values=pixel_values)
+        image_embeds = self.multi_modal_projector(image_features)
+        return [
+            e.flatten(0, 2) for e in image_embeds.split(num_patches.tolist())
+        ]
+
+    def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
+        h = w = self.config.vision_config.image_size
+        expected_dims = (3, h, w)
+
+        def _validate_shape(d: torch.Tensor):
+            if d.shape != expected_dims:
+                raise ValueError(
+                    "The expected shape of pixel values per image per batch "
+                    f"is {expected_dims}. You supplied {tuple(d.shape)}.")
+
+        for d in data:
+            _validate_shape(d)
+
+        return data
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[AyaVisionImagePixelInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        num_patches = kwargs.pop("num_patches", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        assert image_embeds is None, "Aya Vision does not support image_embeds."
+
+        if not isinstance(pixel_values, (torch.Tensor, list)):
+            raise ValueError("Incorrect type of pixel values. "
+                             f"Got type: {type(pixel_values)}")
+        if num_patches is not None and not isinstance(num_patches,
+                                                      (torch.Tensor, list)):
+            raise ValueError("Incorrect type of num_patches. "
+                             f"Got type: {type(num_patches)}")
+
+        pixel_values = flatten_bn(pixel_values, concat=True)
+        num_patches = flatten_bn(num_patches, concat=True)
+
+        return AyaVisionImagePixelInputs(
+            type="pixel_values",
+            pixel_values=self._validate_pixel_values(pixel_values),
+            num_patches=num_patches,
+        )
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+
+        return self._process_image_input(image_input, **kwargs)
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids=input_ids,
+                inputs_embeds=inputs_embeds,
+                multimodal_embeddings=multimodal_embeddings,
+                placeholder_token_id=self.config.image_token_index,
+            )
+
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        # NOTE: In v1, inputs_embeds is always generated at model runner, this
+        # condition is for v0 compatibility.
+        elif inputs_embeds is None:
+            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)